Breath-holding as a novel approach to risk stratification in COVID-19

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Abstract

Background

Despite considerable progress, it remains unclear why some patients admitted for COVID-19 develop adverse outcomes while others recover spontaneously. Clues may lie with the predisposition to hypoxemia or unexpected absence of dyspnea (‘silent hypoxemia’) in some patients who later develop respiratory failure. Using a recently-validated breath-holding technique, we sought to test the hypothesis that gas exchange and ventilatory control deficits observed at admission are associated with subsequent adverse COVID-19 outcomes (composite primary outcome: non-invasive ventilatory support, intensive care admission, or death).

Methods

Patients with COVID-19 ( N = 50) performed breath-holds to obtain measurements reflecting the predisposition to oxygen desaturation ( mean desaturation after 20-s) and reduced chemosensitivity to hypoxic-hypercapnia (including maximal breath-hold duration). Associations with the primary composite outcome were modeled adjusting for baseline oxygen saturation, obesity, sex, age, and prior cardiovascular disease. Healthy controls ( N = 23) provided a normative comparison.

Results

The adverse composite outcome (observed in N = 11/50) was associated with breath-holding measures at admission (likelihood ratio test, p = 0.020); specifically, greater mean desaturation (12-fold greater odds of adverse composite outcome with 4% compared with 2% desaturation, p = 0.002) and greater maximal breath-holding duration (2.7-fold greater odds per 10-s increase, p = 0.036). COVID-19 patients who did not develop the adverse composite outcome had similar mean desaturation to healthy controls.

Conclusions

Breath-holding offers a novel method to identify patients with high risk of respiratory failure in COVID-19. Greater breath-hold induced desaturation (gas exchange deficit) and greater breath-holding tolerance (ventilatory control deficit) may be independent harbingers of progression to severe disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03630-5.

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Most cited references 32

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Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Fei Zhou, Ting Yu, Ronghui Du … (2020)

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

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Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China

Qiurong Ruan, Kun Yang, Wenxia Wang … (2020)

Dear Editor, The rapid emergence of COVID-19 in Wuhan city, Hubei Province, China, has resulted in thousands of deaths [1]. Many infected patients, however, presented mild flu-like symptoms and quickly recover [2]. To effectively prioritize resources for patients with the highest risk, we identified clinical predictors of mild and severe patient outcomes. Using the database of Jin Yin-tan Hospital and Tongji Hospital, we conducted a retrospective multicenter study of 68 death cases (68/150, 45%) and 82 discharged cases (82/150, 55%) with laboratory-confirmed infection of SARS-CoV-2. Patients met the discharge criteria if they had no fever for at least 3 days, significantly improved respiratory function, and had negative SARS-CoV-2 laboratory test results twice in succession. Case data included demographics, clinical characteristics, laboratory results, treatment options and outcomes. For statistical analysis, we represented continuous measurements as means (SDs) or as medians (IQRs) which compared with Student’s t test or the Mann–Whitney–Wilcoxon test. Categorical variables were expressed as numbers (%) and compared by the χ 2 test or Fisher’s exact test. The distribution of the enrolled patients’ age is shown in Fig. 1a. There was a significant difference in age between the death group and the discharge group (p < 0.001) but no difference in the sex ratio (p = 0.43). A total of 63% (43/68) of patients in the death group and 41% (34/82) in the discharge group had underlying diseases (p = 0.0069). It should be noted that patients with cardiovascular diseases have a significantly increased risk of death when they are infected with SARS-CoV-2 (p < 0.001). A total of 16% (11/68) of the patients in the death group had secondary infections, and 1% (1/82) of the patients in the discharge group had secondary infections (p = 0.0018). Laboratory results showed that there were significant differences in white blood cell counts, absolute values of lymphocytes, platelets, albumin, total bilirubin, blood urea nitrogen, blood creatinine, myoglobin, cardiac troponin, C-reactive protein (CRP) and interleukin-6 (IL-6) between the two groups (Fig. 1b and Supplementary Table 1). Fig. 1 a Age distribution of patients with confirmed COVID-19; b key laboratory parameters for the outcomes of patients with confirmed COVID-19; c interval from onset of symptom to death of patients with confirmed COVID-19; d summary of the cause of death of 68 died patients with confirmed COVID-19 The survival times of the enrolled patients in the death group were analyzed. The distribution of survival time from disease onset to death showed two peaks, with the first one at approximately 14 days (22 cases) and the second one at approximately 22 days (17 cases) (Fig. 1c). An analysis of the cause of death was performed. Among the 68 fatal cases, 36 patients (53%) died of respiratory failure, five patients (7%) with myocardial damage died of circulatory failure, 22 patients (33%) died of both, and five remaining died of an unknown cause (Fig. 1d). Based on the analysis of the clinical data, we confirmed that some patients died of fulminant myocarditis. In this study, we first reported that the infection of SARS-CoV-2 may cause fulminant myocarditis. Given that fulminant myocarditis is characterized by a rapid progress and a severe state of illness [3], our results should alert physicians to pay attention not only to the symptoms of respiratory dysfunction but also the symptoms of cardiac injury. Further, large-scale studies and the studies on autopsy are needed to confirm our analysis. In conclusion, predictors of a fatal outcome in COVID-19 cases included age, the presence of underlying diseases, the presence of secondary infection and elevated inflammatory indicators in the blood. The results obtained from this study also suggest that COVID-19 mortality might be due to virus-activated “cytokine storm syndrome” or fulminant myocarditis. Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary material 1 (DOCX 38 kb)

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Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection

Adam Bernheim, Xueyan Mei, Mingqian Huang … (2020)

Abstract In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, “crazy-paving” pattern and the “reverse halo” sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).

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Author and article information

Contributors

Ludovico Messineo:

ORCID: http://orcid.org/0000-0002-4142-9124

ludovico.messineo@yahoo.it

Journal

Journal ID (nlm-ta): Crit Care

Title: Critical Care

Publisher: BioMed Central (London )

ISSN (Print): 1364-8535

ISSN (Electronic): 1466-609X

Publication date (Electronic): 14 June 2021

Publication date PMC-release: 14 June 2021

Publication date Collection: 2021

Volume: 25

Electronic Location Identifier: 208

Affiliations

[1 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, , Brigham & Women’s Hospital & Harvard Medical School, ; Boston, MA USA

[2 ]GRID grid.1014.4, ISNI 0000 0004 0367 2697, Adelaide Institute for Sleep Health (AISH), Flinders Health and Medical Research Institute (FHMRI), , Flinders University, ; 5 Laffer Drive, Bedford Park, Adelaide, SA 5043 Australia

[3 ]GRID grid.418224.9, ISNI 0000 0004 1757 9530, Istituto Auxologico Italiano IRCSS, Sleep Medicine Center, Department of Cardiology, , San Luca Hospital, ; Milano, Italy

[4 ]GRID grid.7563.7, ISNI 0000 0001 2174 1754, Department of Medicine and Surgery, , University of Milano-Bicocca, ; Milan, Italy

[5 ]GRID grid.7637.5, ISNI 0000000417571846, Respiratory Medicine and Sleep Laboratory, Department of Experimental and Clinical Sciences, , University of Brescia and Spedali Civili, ; Brescia, Italy

[6 ]GRID grid.412725.7, Department of Internal Medicine, , Spedali Civili, ; Brescia, Italy

[7 ]GRID grid.416060.5, ISNI 0000 0004 0390 1496, Monash Lung and Sleep, , Monash Medical Centre, ; Clayton, VIC Australia

[8 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, School of Clinical Sciences, , Monash University, ; Melbourne, VIC Australia

[9 ]Monash Partners – Epworth, Victoria, Australia

[10 ]Maugeri Institute IRCCS, Sleep Medicine Center, Pavia, Italy

[11 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, School of Information Technology and Electrical Engineering, , The University of Queensland, ; Brisbane, Australia

[12 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, University of California San Diego, ; La Jolla, CA USA

[13 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Department of Allergy Immunology and Respiratory Medicine and Central Clinical School, , The Alfred and Monash University, ; Melbourne, Australia

Author information

Ludovico Messineo http://orcid.org/0000-0002-4142-9124

Article

Publisher ID: 3630

DOI: 10.1186/s13054-021-03630-5

PMC ID: 8200551

PubMed ID: 34127052

SO-VID: 9a560aa6-7534-47d6-a22c-e1cd4b94a060

License:

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